publication . Preprint . Other literature type . 2018

Kipoi: accelerating the community exchange and reuse of predictive models for genomics

Avsec, Žiga; Kreuzhuber, Roman; Israeli, Johnny; Xu, Nancy; Cheng, Jun; Shrikumar, Avanti; Banerjee, Abhimanyu; Kim, Daniel S.; Urban, Lara; Kundaje, Anshul; ...
Open Access English
  • Published: 24 Jul 2018
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:title>Abstract</jats:title><jats:p>Advanced machine learning models applied to large-scale genomics datasets hold the promise to be major drivers for genome science. Once trained, such models can serve as a tool to probe the relationships between data modalities, including the effect of genetic variants on phenotype. However, lack of standardization and limited accessibility of trained models have hampered their impact in practice. To address this, we present Kipoi, a collaborative initiative to define standards and to foster reuse of trained models in genomics. Already, the Kipoi repository contains over 2,000 trained models that cover canonical predictio...
Funded by
NIH| Decoding the regulatory architecture of the human genome across cell types, individuals and disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U01HG009431-03
  • Funding stream: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
,
EC| PanCanRisk
Project
PanCanRisk
Personalized bioinformatics for global cancer susceptibility identification and clinical management
  • Funder: European Commission (EC)
  • Project Code: 635290
  • Funding stream: H2020 | RIA

1. Luo, R., Sedlazeck, F. J., Lam, T.-W. & Schatz, M. Clairvoyante: a multi-task 9. Rosenberg, A. B., Patwardhan, R. P., Shendure, J. & Seelig, G. Learning the sequence 10. Paggi, J. M. & Bejerano, G. A sequence-based, deep learning model accurately predicts 20. PyTorch. Available at: ​https://pytorch.org/.​ (Accessed: 23rd May 2018) 30. Pawlowski, N., Caicedo, J. C., Singh, S., Carpenter, A. E. & Storkey, A. Automating 32. Kelley, D. R., Snoek, J. & Rinn, J. L. Basset: learning the regulatory code of the http://dreamchallenges.org/.​ (Accessed: 2nd July 2018) 43. Critical Assessment of Genome Interpretation |. Available at: https://genomeinterpretation.org/.​ (Accessed: 2nd July 2018) 44. Köster, J. & Rahmann, S. Snakemake-a scalable bioinformatics workflow engine. 46. Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. ​arXiv [cs.LG] 47. Kircher, M. ​et al.​ A general framework for estimating the relative pathogenicity of human 49. Jian, X., Boerwinkle, E. & Liu, X. In silico prediction of splice-altering single nucleotide [OpenAIRE]

Abstract
<jats:title>Abstract</jats:title><jats:p>Advanced machine learning models applied to large-scale genomics datasets hold the promise to be major drivers for genome science. Once trained, such models can serve as a tool to probe the relationships between data modalities, including the effect of genetic variants on phenotype. However, lack of standardization and limited accessibility of trained models have hampered their impact in practice. To address this, we present Kipoi, a collaborative initiative to define standards and to foster reuse of trained models in genomics. Already, the Kipoi repository contains over 2,000 trained models that cover canonical predictio...
Funded by
NIH| Decoding the regulatory architecture of the human genome across cell types, individuals and disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U01HG009431-03
  • Funding stream: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
,
EC| PanCanRisk
Project
PanCanRisk
Personalized bioinformatics for global cancer susceptibility identification and clinical management
  • Funder: European Commission (EC)
  • Project Code: 635290
  • Funding stream: H2020 | RIA

1. Luo, R., Sedlazeck, F. J., Lam, T.-W. & Schatz, M. Clairvoyante: a multi-task 9. Rosenberg, A. B., Patwardhan, R. P., Shendure, J. & Seelig, G. Learning the sequence 10. Paggi, J. M. & Bejerano, G. A sequence-based, deep learning model accurately predicts 20. PyTorch. Available at: ​https://pytorch.org/.​ (Accessed: 23rd May 2018) 30. Pawlowski, N., Caicedo, J. C., Singh, S., Carpenter, A. E. & Storkey, A. Automating 32. Kelley, D. R., Snoek, J. & Rinn, J. L. Basset: learning the regulatory code of the http://dreamchallenges.org/.​ (Accessed: 2nd July 2018) 43. Critical Assessment of Genome Interpretation |. Available at: https://genomeinterpretation.org/.​ (Accessed: 2nd July 2018) 44. Köster, J. & Rahmann, S. Snakemake-a scalable bioinformatics workflow engine. 46. Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. ​arXiv [cs.LG] 47. Kircher, M. ​et al.​ A general framework for estimating the relative pathogenicity of human 49. Jian, X., Boerwinkle, E. & Liu, X. In silico prediction of splice-altering single nucleotide [OpenAIRE]

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